Latest news with #Processing
Business Insider
a day ago
- Business
- Business Insider
‘Don't Bet the Farm,' Says Analyst About Quantum Computing Stock (QUBT)
Just months after suggesting that the widespread adoption of quantum computing was still '15 to 20 years away,' Nvidia CEO Jensen Huang offered a much more optimistic outlook, sparking a rally in speculative quantum stocks, such as Quantum Computing, Inc. (QUBT). The tech pioneer is up 8.5% so far this week, with bullish sentiment at its peak. Confident Investing Starts Here: However, a deeper look reveals significant concerns about QUBT's financial position. And while the industry is making progress, scaling quantum systems to tackle real-world problems remains a massive challenge, particularly for a company of QUBT's size. Despite the broader enthusiasm for 'lifting all boats' in the sector, I remain bearish on QUBT. The Nvidia Effect: A CEO's Shifting Narrative Being the CEO of a major tech company comes with significant influence—something Nvidia's Jensen Huang demonstrated at the GTC Paris developer conference when he declared that 'quantum computing is reaching an inflection point.' While his remarks centered on Nvidia's own innovations—like CUDA-Q, which aims to integrate quantum capabilities with classical systems—his optimism could have ripple effects across the sector. Nvidia backed its words with action in March 2025 by launching a new quantum computing research lab in Boston, reinforcing its leadership in the space. Huang's bullish tone may inspire increased venture capital and R&D investment across the quantum ecosystem. However, skepticism persists. Many still view practical quantum applications as decades away, with the industry struggling to define clear, real-world use cases that outperform traditional supercomputers. Without a breakthrough and tangible return on investment (ROI), quantum computing remains a tough sell to potential customers seeking immediate, measurable benefits. Quantum Computing's Niche Technology Quantum Computing Inc. specializes in photonic, or light-based, quantum solutions, developing Quantum Processing Units (QPUs) designed to operate at room temperature and low power, features that could make the technology more accessible and cost-effective. However, the company's focus remains on niche applications, such as remote sensing and computational chemistry, which limits its current market reach. While progress is being made in identifying use cases where quantum systems may outperform classical supercomputers, practical, scalable, and commercially viable applications are still emerging. The technology faces persistent challenges, including qubit fragility, high error rates, and scalability limitations. These machines are highly specialized and complex, suited for addressing targeted, advanced problems, but are not yet ready for broad commercial deployment. Financials Tell A Different Story Quantum Computing's first-quarter 2025 earnings highlight just how early-stage its business remains. The company reported revenue of only $39,000—roughly equivalent to the median U.S. individual income—while operating expenses climbed to $8.3 million. A $23.6 million non-cash gain from the mark-to-market revaluation of its warrant liability resulted in a reported net income of $17 million. However, this masks the company's ongoing operational losses. On the operational front, the company completed construction of its Quantum Photonic Chip Foundry. It announced new partnerships, including a contract with NASA's Langley Research Center—a sign of growing institutional interest despite modest commercial traction so far. QUBT's Speculative Valuation Quantum Computing's ~$3 billion market cap, despite minimal revenue, highlights an apparent disconnect from fundamentals and suggests the stock is driven largely by speculation. While the company holds $166 million in cash and cash equivalents, providing it with some runway to develop its technology, its R&D budget is modest compared to that of deep-pocketed rivals like IBM, Google, Microsoft, and Nvidia. Importantly, this cash position was built primarily through dilutive stock offerings and private placements, underscoring its heavy reliance on external funding. Given these constraints, it's difficult to envision a near-term path where Quantum Computing scales its niche technology into a broadly commercial product in a way that meaningfully rewards shareholders. Is QUBT Stock a Buy, Hold, or Sell? Reflecting its speculative nature, Quantum Computing's analyst coverage is limited. Its Moderate Buy consensus rating is based on one Buy recommendation in the past three months. Its average price target of $14.00 implies a downside potential of ~27% over the next 12 months. Meanwhile, TipRanks AI assigns QUBT a Neutral rating and a price target of $22. It notes that Quantum Computing's strong balance sheet and Qatalyst software positions it favorably amid hardware advances and increasing demand. However, it also points out that QUBT sports a high valuation, especially in light of ongoing losses and minimal revenues. QUBT Remains a High-Risk Bet in a Competitive Field While quantum computing as a whole may be approaching an 'inflection point,' the outlook for pure-play firms like Quantum Computing Inc. remains highly speculative. With minimal revenue, the company is still far from its own inflection point, where its products gain broad commercial viability. Reaching that stage will likely require scientific breakthroughs and significant R&D investment, which Quantum Computing may struggle to match relative to well-funded giants like IBM. That said, growing industry momentum is a clear tailwind. Rising interest in the sector could lead to increased funding, larger contracts, and a stronger push toward practical applications. Quantum Computing's unique focus on room-temperature, low-power photonic quantum systems, along with its early, albeit modest, commercial traction, may appeal to risk-tolerant, long-term investors. Personally, I remain highly cautious. The company's weak financial performance, lofty valuation, and limited ability to compete with larger players make its long-term investment case difficult to justify at this stage.
Indian Express
2 days ago
- Business
- Indian Express
Midjourney releases V1, its first AI video generation model available to all users
AI research lab Midjourney has rolled out its first-ever, text-to-video generation model called V1. The San Francisco-based firm on June 18, said that V1 can be used to convert images into a five-second AI-generated video clip. Users can either upload the images or use an AI-generated image by Midjourney itself. Then, the user can click 'animate' to animate the image. This creates four five-second AI-generated clips, which can be individually extendable to 20 seconds. It is unclear if these clips will also have sound. 'The inevitable destination of this technology are models capable of real-time open-world simulations. What's that? Basically; imagine an AI system that generates imagery in real-time,' Midjourney CEO David Holz said in a statement. The animations generated through V1 can be either Automatic or Manual. In Automatic mode, the AI tool suggests a motion prompt to the user in order to make the image move while the Manual setting requires users to input prompts based on how they want the image to move and the scene to develop. The user can also choose the camera style of the AI-generated video clip. The low-motion style is a stationary camera setting with slower camera movements while the high motion setting is a much more active camera setting, with the subject and the camera showing motion throughout the AI-generated animation. Midjourney has made V1 accessible across all tiers, which means even free users of the platform can use the AI tool to create video clips. However, Midjourney said that creating a video will cost eight times more Graphics Processing Unit (GPU) time to the user as compared to generating still images. 'This is amazing, surprising, and over 25 times cheaper than what the market has shipped before. It will only improve over time,' as per the AI firm. Users can access V1 in 'fast mode' or 'relax mode'. Fast mode entails using a set GPU time received every month. This is the mode available to all free and paid users, with one minute being used for image generation, and eight minutes used for video generation. Once this runs out, users cannot create any further AI-generated content on Midjourney. Relax mode for videos is currently being tested for Midjourney Pro subscribers and above. It allows for unlimited GPU time. While image and video generation is unlimited, 'relax mode' takes longer (up to 10 minutes) as prompts wait in line to be completed.
Hindustan Times
4 days ago
- Business
- Hindustan Times
Access to GPUs is a national concern: Gnani AI CEO Ganesh Gopalan
The biggest issue artificial intelligence (AI) companies face today is access to compute and the cost to building large language models (LLMs), Ganesh Gopalan, CEO and founder of Gnani AI, said during an interaction with HT. Selected under the Ministry of Electronics and Information technology's (MeitY) 'India AI Mission' to build a foundational model for India, Gnani AI's CEO said the government is yet to clarify how much AI compute will be subsidised or what other benefits the company can expect under the programme. Access to Graphics Processing Units (GPUs) is a national concern, Gopalan told HT over a call, noting that more advanced versions of GPUs are continually being released. 'Some of the newer GPUs have much more computational power, but if they are going to be available after some time, is it worth waiting for them,' he said. Under the India AI Mission, the government has selected four startups — Gnani AI, Sarvam AI, Soket AI, and Gan AI — from a pool of 506 proposals to build home-grown foundational models. How will these foundational models differ from OpenAI's GPT or Meta's Llama? They will be built from the ground up using India-specific datasets, languages, and cultural contexts. While there is still some ambiguity within startups around the exact support being offered, MeitY secretary Abhishek Singh said in an April podcast that, in addition to nearly fully subsidising compute, the government will also cover costs related to engineering, personnel, and data. According to India AI's compute portal, Sarvam AI has received 4,096 fully subsidised GPUs, valued at over ₹246 crore. In total, the government has allocated 4,423 GPUs under the scheme till now, with a cumulative subsidy amounting to ₹259.89 crore. The rest of the subsidies have been allocated to researchers, early stage startups and government entities. The government has also called rounds of GPU empanelment, in which by now they have a capacity of over 34,000 GPUs, of which they have made 14,000 GPUs available online on their platform. Gopalan along with Ananth Nagaraj started the company seven years ago. Fast forward to 2025, Gnani AI has over 100 clients and is building 14 billion-parameter voice-to-voice foundational AI models for India, aimed at enabling instant and natural conversations without human intervention. The company plans to initially launch a model supporting 14 languages, gradually expanding to cover 22 languages. The startup has a three-pronged aim: reduce latency, increase accuracy and infuse emotional intelligence in their LLM. 'The models aim to enable emotionally aware conversations, preserving intonation, stress and rhythm in the conversations. The model uses a fused architecture to reduce inherent latencies and errors that cascade through the pipeline,' explained Gopalan. Gnani AI has built a substantial dataset of 14 million hours of annotated audio, with the team initially spending two to three years collecting and curating the first set of data. 'We collected a lot of data all across India when we started in 2016, often at 1/1000 the cost of what a large company would collect it at because we were a hungry startup without money,' said Gopalan jokingly. 'The dialects change in India every few kilometers. We would find out which districts we haven't collected data from and we would go there and collect it.' When asked whether the company buys data, the CEO responded, 'Why should I buy data for $100 an hour when we know how to get it for ₹100 an hour?' Gnani AI collects data using many methods like using open-source datasets, working with language experts for lesser-resourced languages, and building proprietary data through a wide range of gamified mobile apps to collect diverse speech data for training its models. 'We sometimes also requested people to send over their voice through WhatsApp. Some of those were also given free by people, when we told them 'look, we are building this (LLM) for your language,' said Gopalan. His team is also looking at AI Kosh, a government platform where government and private datasets have been made available. While Sarvam AI said in April that it will come out with it foundational model in six months, Soket AI Labs has set a one-year delivery timeline. Gnani has set a timeline of six to eight months to deliver the first version of its model once it receives access to compute resources from the government. Other companies are also showing interest in building foundational models for India. TWO CEO Pranav Mistry confirmed to HT that he has submitted his foundational model proposal to the India AI Mission.
New Indian Express
07-06-2025
- Business
- New Indian Express
'AI models can hallucinate or misfire'
Artificial Intelligence (AI) offers immense potential, but it's not without challenges. Mohit Saxena, Co-Founder & CTO, InMobi & Glance told TNIE that there's the critical need for human oversight and that AI models can hallucinate or misfire, and in today's sensitive digital climate, ensuring responsible output is essential. 'We're investing in rigorous moderation infrastructure and developing new governance frameworks to mitigate these risks,' he said. He added that deep AI expertise is scarce. 'While surface-level applications like RAG (Retrieval-Augmented Generation) are becoming common, true innovation requires depth in data science, ML infrastructure, and systems thinking—talent that's still hard to find.' But our global presence in Bengaluru, San Francisco, and the UK gives us broader access to specialised talent pools, the co-founder said. Talking about other key challenges, he said that AI infrastructure is expensive. Running advanced models at scale demands significant compute and energy. 'Our approach is rooted in frugality—we optimize model usage, leverage pre-processing, explore alternatives like TPUs (Tensor Processing Unit), and work closely with partners like Google to get the most out of every dollar,' he said. InMobi views AI not just as a tool, but as a foundational shift and its roadmap over the next one to three years is anchored in three key areas. 'First, we are reimagining engineering productivity with AI—helping experienced engineers scale faster and empowering fresh talent to leapfrog traditional learning curves. AI is now embedded into every aspect of how we build—whether it's writing code, improving observability, or boosting efficiency,' he said. 'Second, we are building intelligent automation into our core business processes—moving from simple scripting to AI agents that can deconstruct complex workflows, predict outcomes, and take action. This isn't just automation; it's autonomous decision-making at scale. Third, we're embracing the rise of agentic architecture—where agents talk to agents, not APIs (Application Programming Interface), to get work done. This is the future of system communication, and are actively developing for it,' he further said. InMobi is setting up a dedicated unit to track and accelerate engineering efficiency with AI, with a goal to complete most of the foundational work by year-end. The company is leveraging AI to generate high-impact formats—ranging from image-based ads to audio creatives—enabling brands to engage users across multiple touchpoints. It also uses AI to generate and summarize content at scale. In the visual content space, he said the company is leveraging Contrastive Language-Image Pre-training (CLIP) to bridge the gap between AI-generated creativity and real-world commerce through its Glance AI product. 'By using CLIP, we're able to understand and interpret AI-generated fashion looks—essentially decoding the visual style and identifying apparel elements within the image. These elements are then matched to real products from our extensive catalogue of brand and retail partners,' he explained. Even before the LLM (large language model) wave, the company has been leveraging AI for content generation at Glance. 'We're onboarding fresh engineering talent through structured bootcamps where AI adoption starts from day one—including access to AI assistants and hands-on experience with applied ML tools. Simultaneously, we're deepening our bench strength by hiring top-tier data scientists—we've onboarded over 50 employees in the past year alone, across domains like LLMs, DNNs (Deep Neural Networks), and imaging. We're also shifting our hiring lens—prioritising engineers with a strong aptitude in data science and statistical thinking. Our aim is that 80% of our workforce, both new and existing, to be highly AI- and ML-savvy in the next 1–2 years,' the co-founder and CTO informed.
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Business Standard
05-06-2025
- Business
- Business Standard
India's IT giants can't afford to sit out the AI race, says senior official
While startups and academic researchers have so far led India's push to build foundational artificial intelligence (AI) models, the country's legacy IT giants are unlikely to remain on the sidelines for long, according to Abhishek Singh, Additional Secretary at the Ministry of Electronics and Information Technology (MeitY). 'Given the capability that our IT industry has and what we are hearing about Infosys and TCs, all of them are working on AI-based applications,' Singh said at the Accel AI Summit, 2025 on Wednesday in Bengaluru. 'There will be a need for doing this and enhancing their capabilities.' Infosys and TCS have both begun ramping up investments in high-performance computing infrastructure, including Graphics Processing Units (GPUs), while simultaneously retraining thousands of employees in AI-related skills. Singh noted that these companies are already supporting global clients with AI-driven applications, and it's only a matter of time before they begin to roll out proprietary AI products and platforms. 'Otherwise for them to survive without AI, it will be very difficult,' he said. 'The whole world is going towards the adoption of AI.' He said whether it's software delivery, coding practices, SaaS implementation, or solutioning for enterprise clients, everything is being reshaped by AI. Singh believes the transformation underway across the global IT landscape will force India's large tech players to shift from primarily service-driven models to product-led AI innovation. The demand for intelligent applications and automation is only accelerating. And given their scale and customer base, India's top IT firms are well-positioned to deliver on that demand—if they move swiftly. While traditional IT services companies may not be leading the development of India's foundational AI models, they are quietly catalyzing a wave of innovation through internal transitions and ecosystem partnerships, according to Prashanth Prakash, founding partner of Accel India. Inside many large firms, there's an active rethink underway about what comes next in the age of AI. One notable trend, he noted, is senior leadership exiting established IT companies to launch or support new AI ventures. These leaders already understand enterprise pain points and high-impact use cases. They bring domain expertise and built-in demand. 'They're now partnering with VCs to build startups from the ground up,' he said. Another model emerging is through Global Capability Centers (GCCs) based in India. These units are increasingly approaching startups with concrete enterprise use cases and positioning themselves as early partners. They are asking the startups to take their workflows and reimagine them using AI agents or automation. This convergence of talent migration, early-stage venture activity, and enterprise-driven use case development, signals a deeper transformation within India's services ecosystem. 'I think we're seeing multiple ways in which there will be disruption within the context of the Indian services ecosystem,' said Prakash. AI regulations As artificial intelligence applications proliferate across sectors, the Indian government is also taking a pragmatic approach to regulation—aiming to ensure compliance with existing legal frameworks while avoiding heavy-handed restrictions that could stifle innovation, according to Singh of MeitY. 'Regulation will primarily be to ensure that any AI application that is developed is compliant with the legal framework and the laws as they stand,' Singh said. 'But we are not inclined to have something similar to the European Union's approach.' Instead, Singh explained, the government will rely on sector-specific and harm-based safeguards anchored in current statutes—such as the Digital Personal Data Protection Act (DPDP), the Information Technology Act, and the Bharatiya Nyaya Sanhita, which recently replaced the Indian Penal Code. Citing examples, Singh emphasised that generative AI models must be sensitive to local legal contexts. He gave the example of pre-natal sex determination, which is strictly prohibited in India. Yet, an AI model trained on global datasets might identify the sex of a fetus from an ultrasound image, violating Indian law. He said developers will need to ensure such models are adapted to local legal and ethical norms. The focus, Singh added, will be on preventing harm—such as the spread of deepfakes, misinformation, or content that could incite violence or infringe on individual rights. He was of the view that if an application risks violating the law or causing harm to individuals or communities, it will be regulated. However, Singh was clear that regulation would not come at the cost of technological progress. 'We are more inclined towards promoting AI application development rather than restricting it.'
